SYSTRAN, a global leader in language translation technology, showcased its newest translation software, called Pure Neural Machine Translation (PNMT), at the InnoXcell Annual Symposium focused on China-U.S. regulatory compliance and eDiscovery in New York City this month.
As an exhibitor and sponsor of the event, SYSTRAN’s team provided attendees an exclusive view of the PNMT concept and how it can be utilized by the legal and regulatory compliance industries to boost productivity and cut translation costs.

“Neural Machine Translation ushers in a new era for Language Productivity Tools, making MT a genuine alternative to human translation,” says Ken Behan, Vice President of Sales and Marketing of SYSTRAN. “InnoXcell attendees will have a great opportunity to increase linguistic productivity in the GRC and eDiscovery world with this technology.”

Unlike statistical (SMT) or rule-based (RMT) translation engines, NMT engines process an entire sentence, paragraph or document taking into context the topic being discussed. The NMT engine models the whole process of machine translation through a unique artificial neural network, working similar to a human brain. The entire chain is processed end-to-end with no intermediate stages between the source sentence and the target, providing for more accurate translations.

As the legal industry increasingly works with multilingual content, such as emails from global companies, firms need a way to translate content quickly, reliably and cost-effectively. SYSTRAN’s software driven by machine translation provides organizations the ability to automatically translate audio and text in more than 130 different language pairs. The software can also be leveraged to perform real-time translation on intranets and other tools. At any given moment, users can translate entire sites, blogs or document to find and understand foreign-language info in real-time.

The InnoXcell conference took place in New York, NY on December 6. Attendees were able to learn more about the PNMT concept, how it works and what it will allow companies to do. To set up a meeting or schedule a demo, contact Craig Stern at craig.stern@systrangroup.com.

About SYSTRAN

For over 48 years, SYSTRAN transformed the way global organizations such as Apple, Adobe, Daimler, HSBC, and Symantec meet the challenges of communicating globally via advanced machine-based translation technology. With the ability to facilitate communication in over 130 languages and 20 vertical domains, SYSTRAN enables instantaneous and automatic multilingual translations for texts, emails, chat, web pages, mobile apps, documents, user-generated content and more.

SYSTRAN, a global leader in language translation technology, presented at the Association for Machine Translation in America (AMTA) conference in Austin this month. The talk, titled "Building Renewable Language Assets in Government Domains," included insights from the company's latest efforts in Neural Machine Translation.

The presentation, given by Beth Flaherty, SYSTRAN's Director of Government Solutions, and Joshua Johanson, a computational linguist with SYSTRAN, discussed the company's work in specific domains and languages of interest to the government.

"SYSTRAN welcomes this opportunity to share our accomplishments to date with the government community," says Flaherty. "We will also reveal some of our plans to integrate neural network technology into our offerings to further serve the public sector with faster, smarter machine translation."

As demand for multilingual content continues to increase, public-sector organizations struggle to produce content efficiently, reliably and cost-effectively. SYSTRAN's software driven by machine translation provides organizations the ability to automatically translate audio and text in more than 130 different language pairs. The software can also be leveraged to perform real-time translation on intranets and other tools. At any given moment, users can translate entire sites, blogs or document to find and understand foreign language information in real-time.

The AMTA conference took place in Austin, TX from October 28 through November 1. SYSTRAN's presentation was scheduled for October 31 from 5 – 5:30 p.m. on the Government track. The company also exhibited at the Technology showcase on October 30 from 12:30 – 3:30 p.m.

The results obtained from Neural Machine Translation are amazing, in particular, the neural network’s paraphrasing. It almost seems as if the neural network really “understands” the sentence to translate. In this first article, we are interested in “meaning,” that which gives an idea of the type of semantic knowledge the neural networks use to translate.

Let us start with a glimpse of how the 3 technologies work, the different steps of each translation process and the resources that each technology uses to translate. Then we will take a look at a few examples and compare what each technology must do to translate them correctly.

by Kirti Vashee on eMpTy Pages, a blog about translation technology, localization and collaboration

[…] So, I recently had a conversation with Jean Senellart , Global CTO and SYSTRAN SAS Director General, to find out more about their new NMT technology. He was very forthcoming, and responded to all my questions with useful details, anecdotes and enthusiasm. The conversation only reinforced in my mind that “real MT system development” is something best left to experts, and not something that even large LSPs should dabble with. The reality and complexity of NMT development pushes the limits of MT even further away from the DIY mirage.

In the text below, I have put quotes around everything that I have gotten directly from SYSTRAN material or from Jean Senellart (JAS) to make it clear that I am not interpreting. I have done some minor editing to facilitate readability and “English flow” and added comments in italics within his quotes where this is done.

The first engine to be based on neural models and deep learning, delivering unparalleled translation quality!

Project “PNMT” (Purely Neural Machine Translation) was this year’s flagship project for the researchers and developers at SYSTRAN, the leading provider in machine translation and natural language processing, confirming its pioneer position for over 40 years.

SYSTRAN brings its expertise to the sector in several ways: contributing to research on neural models; applying its know-how in terminology to increase the potential of Neural Machine Translation; and industrializing technology to make it available to companies, organizations and individuals.